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All Journal Jurnal Surya Teknika
Hastuti, Ririn Puji
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Tantangan Implementasi Program B40 di Indonesia: Tinjauan Teknis dan Ketersediaan Bahan Baku Biodiesel Eka Alel, Ariya; Hastuti, Ririn Puji; Legawati, Lisa
SURYA TEKNIKA Vol 12 No 2 (2025): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v12i2.10770

Abstract

The B40 program is a continuation of Indonesia’s mandatory biodiesel policy, which requires blending 40% palm oil–based biodiesel with 60% conventional diesel fuel. This program aims to reduce dependence on fossil fuel imports while supporting national greenhouse gas emission reduction targets. This study analyzes the challenges of B40 implementation in Indonesia, focusing on feedstock availability as well as technical aspects of biodiesel production and distribution. The research employs a desk study approach using secondary data from GAPKI, BPDPKS, the Ministry of Energy and Mineral Resources, and relevant scientific literature. The results indicate that Indonesia’s crude palm oil (CPO) production in the first half of 2025 reached approximately 27.89 million tons, while the CPO requirement for B40 is projected at around 13.5 million tons. The installed capacity of the national biodiesel industry is about 20 million kiloliters per year, exceeding the projected B40 biodiesel demand of 15.6 million kiloliters. However, technical challenges remain, including biodiesel quality issues, dependence on imported methanol, and distribution constraints in remote regions. Overall, the B40 program has the potential to reduce emissions by approximately 25–28 million tons of CO₂-equivalent per year and contribute to Indonesia’s energy transition.
Machine Learning-Based Prediction of Sustainable Aviation Fuel Yield using Literature-Derived Hydroprocessing Data Eka Alel, Ariya; Hastuti, Ririn Puji; Suhendri, Suhendri; Alfarisi, Cory Dian
SURYA TEKNIKA Vol 13 No 1 (2026): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v13i1.11609

Abstract

The increasing demand for sustainable aviation fuel (SAF) has encouraged the development of efficient predictive approaches for optimizing jet fuel production from renewable feedstocks. Conventional experimental optimization methods are often time-consuming and expensive because hydroprocessing performance is strongly influenced by feedstock characteristics, catalyst composition, and operating conditions. In this study, machine learning (ML) techniques were applied to predict jet fuel yield using a dataset compiled from approximately 50 published scientific articles. The dataset consisted of 101 experimental observations involving different feedstock groups, catalyst metal groups, catalyst supports, catalyst loading, free fatty acid (FFA) content, temperature, pressure, and weight hourly space velocity (WHSV). The ML workflow was developed using Orange Data Mining software and included data preprocessing, feature selection, imputation, model training, and performance evaluation. Four regression algorithms, namely Random Forest, Linear Regression, Neural Network, and Gradient Boosting, were evaluated using 10-fold cross-validation. The Gradient Boosting model achieved the best predictive performance with an RMSE of 7.172, MAE of 5.314, MAPE of 10.026%, and R2 value of 0.286 during cross-validation. Feature ranking analysis indicated that catalyst support type, feedstock group, catalyst metal group, and FFA content were among the most influential variables affecting jet fuel yield.
Co-Authors Alfarisi, Cory Dian Eka Alel, Ariya Lisa Legawati Suhendri Suhendri